Overview

Dataset statistics

Number of variables18
Number of observations891
Missing cells906
Missing cells (%)5.6%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory589.8 KiB
Average record size in memory677.9 B

Variable types

Numeric6
Categorical9
Text3

Alerts

DatasetName has constant value ""Constant
Age has 177 (19.9%) missing valuesMissing
Cabin has 687 (77.1%) missing valuesMissing
CabinPrefix has 40 (4.5%) missing valuesMissing
PassengerId is uniformly distributedUniform
PassengerId has unique valuesUnique
Name has unique valuesUnique
SibSp has 608 (68.2%) zerosZeros
Parch has 678 (76.1%) zerosZeros
Fare has 15 (1.7%) zerosZeros

Reproduction

Analysis started2024-03-23 19:04:48.177037
Analysis finished2024-03-23 19:04:53.730190
Duration5.55 seconds
Software versionydata-profiling vv4.6.5
Download configurationconfig.json

Variables

PassengerId
Real number (ℝ)

UNIFORM  UNIQUE 

Distinct891
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean446
Minimum1
Maximum891
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size7.1 KiB
2024-03-23T16:04:53.823941image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile45.5
Q1223.5
median446
Q3668.5
95-th percentile846.5
Maximum891
Range890
Interquartile range (IQR)445

Descriptive statistics

Standard deviation257.35384
Coefficient of variation (CV)0.57702655
Kurtosis-1.2
Mean446
Median Absolute Deviation (MAD)223
Skewness0
Sum397386
Variance66231
MonotonicityStrictly increasing
2024-03-23T16:04:54.016427image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1 1
 
0.1%
599 1
 
0.1%
588 1
 
0.1%
589 1
 
0.1%
590 1
 
0.1%
591 1
 
0.1%
592 1
 
0.1%
593 1
 
0.1%
594 1
 
0.1%
595 1
 
0.1%
Other values (881) 881
98.9%
ValueCountFrequency (%)
1 1
0.1%
2 1
0.1%
3 1
0.1%
4 1
0.1%
5 1
0.1%
6 1
0.1%
7 1
0.1%
8 1
0.1%
9 1
0.1%
10 1
0.1%
ValueCountFrequency (%)
891 1
0.1%
890 1
0.1%
889 1
0.1%
888 1
0.1%
887 1
0.1%
886 1
0.1%
885 1
0.1%
884 1
0.1%
883 1
0.1%
882 1
0.1%

Survived
Categorical

Distinct2
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Memory size52.3 KiB
0.0
549 
1.0
342 

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters2673
Distinct characters3
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row1.0
3rd row1.0
4th row1.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.0 549
61.6%
1.0 342
38.4%

Length

2024-03-23T16:04:54.170016image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-03-23T16:04:54.405417image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
ValueCountFrequency (%)
0.0 549
61.6%
1.0 342
38.4%

Most occurring characters

ValueCountFrequency (%)
0 1440
53.9%
. 891
33.3%
1 342
 
12.8%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 1782
66.7%
Other Punctuation 891
33.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 1440
80.8%
1 342
 
19.2%
Other Punctuation
ValueCountFrequency (%)
. 891
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 2673
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 1440
53.9%
. 891
33.3%
1 342
 
12.8%

Most occurring blocks

ValueCountFrequency (%)
ASCII 2673
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 1440
53.9%
. 891
33.3%
1 342
 
12.8%

Pclass
Categorical

Distinct3
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Memory size50.6 KiB
3
491 
1
216 
2
184 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters891
Distinct characters3
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row3
2nd row1
3rd row3
4th row1
5th row3

Common Values

ValueCountFrequency (%)
3 491
55.1%
1 216
24.2%
2 184
 
20.7%

Length

2024-03-23T16:04:54.537034image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-03-23T16:04:54.662733image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
ValueCountFrequency (%)
3 491
55.1%
1 216
24.2%
2 184
 
20.7%

Most occurring characters

ValueCountFrequency (%)
3 491
55.1%
1 216
24.2%
2 184
 
20.7%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 891
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
3 491
55.1%
1 216
24.2%
2 184
 
20.7%

Most occurring scripts

ValueCountFrequency (%)
Common 891
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
3 491
55.1%
1 216
24.2%
2 184
 
20.7%

Most occurring blocks

ValueCountFrequency (%)
ASCII 891
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
3 491
55.1%
1 216
24.2%
2 184
 
20.7%

Name
Text

UNIQUE 

Distinct891
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size73.2 KiB
2024-03-23T16:04:54.899070image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/

Length

Max length82
Median length52
Mean length26.965208
Min length12

Characters and Unicode

Total characters24026
Distinct characters60
Distinct categories7 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique891 ?
Unique (%)100.0%

Sample

1st rowBraund, Mr. Owen Harris
2nd rowCumings, Mrs. John Bradley (Florence Briggs Thayer)
3rd rowHeikkinen, Miss. Laina
4th rowFutrelle, Mrs. Jacques Heath (Lily May Peel)
5th rowAllen, Mr. William Henry
ValueCountFrequency (%)
mr 521
 
14.4%
miss 182
 
5.0%
mrs 129
 
3.6%
william 64
 
1.8%
john 44
 
1.2%
master 40
 
1.1%
henry 35
 
1.0%
george 24
 
0.7%
james 24
 
0.7%
charles 23
 
0.6%
Other values (1515) 2538
70.0%
2024-03-23T16:04:55.354847image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
2735
 
11.4%
r 1958
 
8.1%
e 1703
 
7.1%
a 1657
 
6.9%
i 1325
 
5.5%
n 1304
 
5.4%
s 1297
 
5.4%
M 1128
 
4.7%
l 1067
 
4.4%
o 1008
 
4.2%
Other values (50) 8844
36.8%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 15446
64.3%
Uppercase Letter 3645
 
15.2%
Space Separator 2735
 
11.4%
Other Punctuation 1899
 
7.9%
Close Punctuation 144
 
0.6%
Open Punctuation 144
 
0.6%
Dash Punctuation 13
 
0.1%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
r 1958
12.7%
e 1703
11.0%
a 1657
10.7%
i 1325
8.6%
n 1304
8.4%
s 1297
8.4%
l 1067
 
6.9%
o 1008
 
6.5%
t 667
 
4.3%
h 517
 
3.3%
Other values (16) 2943
19.1%
Uppercase Letter
ValueCountFrequency (%)
M 1128
30.9%
A 250
 
6.9%
J 215
 
5.9%
H 203
 
5.6%
S 180
 
4.9%
C 172
 
4.7%
E 166
 
4.6%
W 143
 
3.9%
B 140
 
3.8%
L 129
 
3.5%
Other values (15) 919
25.2%
Other Punctuation
ValueCountFrequency (%)
. 892
47.0%
, 891
46.9%
" 106
 
5.6%
' 9
 
0.5%
/ 1
 
0.1%
Space Separator
ValueCountFrequency (%)
2735
100.0%
Close Punctuation
ValueCountFrequency (%)
) 144
100.0%
Open Punctuation
ValueCountFrequency (%)
( 144
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 13
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 19091
79.5%
Common 4935
 
20.5%

Most frequent character per script

Latin
ValueCountFrequency (%)
r 1958
 
10.3%
e 1703
 
8.9%
a 1657
 
8.7%
i 1325
 
6.9%
n 1304
 
6.8%
s 1297
 
6.8%
M 1128
 
5.9%
l 1067
 
5.6%
o 1008
 
5.3%
t 667
 
3.5%
Other values (41) 5977
31.3%
Common
ValueCountFrequency (%)
2735
55.4%
. 892
 
18.1%
, 891
 
18.1%
) 144
 
2.9%
( 144
 
2.9%
" 106
 
2.1%
- 13
 
0.3%
' 9
 
0.2%
/ 1
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 24026
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
2735
 
11.4%
r 1958
 
8.1%
e 1703
 
7.1%
a 1657
 
6.9%
i 1325
 
5.5%
n 1304
 
5.4%
s 1297
 
5.4%
M 1128
 
4.7%
l 1067
 
4.4%
o 1008
 
4.2%
Other values (50) 8844
36.8%

Sex
Categorical

Distinct2
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Memory size53.8 KiB
male
577 
female
314 

Length

Max length6
Median length4
Mean length4.704826
Min length4

Characters and Unicode

Total characters4192
Distinct characters5
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowmale
2nd rowfemale
3rd rowfemale
4th rowfemale
5th rowmale

Common Values

ValueCountFrequency (%)
male 577
64.8%
female 314
35.2%

Length

2024-03-23T16:04:55.547365image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-03-23T16:04:55.688983image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
ValueCountFrequency (%)
male 577
64.8%
female 314
35.2%

Most occurring characters

ValueCountFrequency (%)
e 1205
28.7%
m 891
21.3%
a 891
21.3%
l 891
21.3%
f 314
 
7.5%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 4192
100.0%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
e 1205
28.7%
m 891
21.3%
a 891
21.3%
l 891
21.3%
f 314
 
7.5%

Most occurring scripts

ValueCountFrequency (%)
Latin 4192
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
e 1205
28.7%
m 891
21.3%
a 891
21.3%
l 891
21.3%
f 314
 
7.5%

Most occurring blocks

ValueCountFrequency (%)
ASCII 4192
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
e 1205
28.7%
m 891
21.3%
a 891
21.3%
l 891
21.3%
f 314
 
7.5%

Age
Real number (ℝ)

MISSING 

Distinct88
Distinct (%)12.3%
Missing177
Missing (%)19.9%
Infinite0
Infinite (%)0.0%
Mean29.699118
Minimum0.42
Maximum80
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size7.1 KiB
2024-03-23T16:04:55.833603image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/

Quantile statistics

Minimum0.42
5-th percentile4
Q120.125
median28
Q338
95-th percentile56
Maximum80
Range79.58
Interquartile range (IQR)17.875

Descriptive statistics

Standard deviation14.526497
Coefficient of variation (CV)0.48912219
Kurtosis0.17827415
Mean29.699118
Median Absolute Deviation (MAD)9
Skewness0.38910778
Sum21205.17
Variance211.01912
MonotonicityNot monotonic
2024-03-23T16:04:56.020105image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
24 30
 
3.4%
22 27
 
3.0%
18 26
 
2.9%
28 25
 
2.8%
30 25
 
2.8%
19 25
 
2.8%
21 24
 
2.7%
25 23
 
2.6%
36 22
 
2.5%
29 20
 
2.2%
Other values (78) 467
52.4%
(Missing) 177
 
19.9%
ValueCountFrequency (%)
0.42 1
 
0.1%
0.67 1
 
0.1%
0.75 2
 
0.2%
0.83 2
 
0.2%
0.92 1
 
0.1%
1 7
0.8%
2 10
1.1%
3 6
0.7%
4 10
1.1%
5 4
 
0.4%
ValueCountFrequency (%)
80 1
 
0.1%
74 1
 
0.1%
71 2
0.2%
70.5 1
 
0.1%
70 2
0.2%
66 1
 
0.1%
65 3
0.3%
64 2
0.2%
63 2
0.2%
62 4
0.4%

SibSp
Real number (ℝ)

ZEROS 

Distinct7
Distinct (%)0.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.52300786
Minimum0
Maximum8
Zeros608
Zeros (%)68.2%
Negative0
Negative (%)0.0%
Memory size7.1 KiB
2024-03-23T16:04:56.163685image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q31
95-th percentile3
Maximum8
Range8
Interquartile range (IQR)1

Descriptive statistics

Standard deviation1.1027434
Coefficient of variation (CV)2.1084644
Kurtosis17.88042
Mean0.52300786
Median Absolute Deviation (MAD)0
Skewness3.6953517
Sum466
Variance1.2160431
MonotonicityNot monotonic
2024-03-23T16:04:56.286388image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
Histogram with fixed size bins (bins=7)
ValueCountFrequency (%)
0 608
68.2%
1 209
 
23.5%
2 28
 
3.1%
4 18
 
2.0%
3 16
 
1.8%
8 7
 
0.8%
5 5
 
0.6%
ValueCountFrequency (%)
0 608
68.2%
1 209
 
23.5%
2 28
 
3.1%
3 16
 
1.8%
4 18
 
2.0%
5 5
 
0.6%
8 7
 
0.8%
ValueCountFrequency (%)
8 7
 
0.8%
5 5
 
0.6%
4 18
 
2.0%
3 16
 
1.8%
2 28
 
3.1%
1 209
 
23.5%
0 608
68.2%

Parch
Real number (ℝ)

ZEROS 

Distinct7
Distinct (%)0.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.38159371
Minimum0
Maximum6
Zeros678
Zeros (%)76.1%
Negative0
Negative (%)0.0%
Memory size7.1 KiB
2024-03-23T16:04:56.408063image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile2
Maximum6
Range6
Interquartile range (IQR)0

Descriptive statistics

Standard deviation0.80605722
Coefficient of variation (CV)2.1123441
Kurtosis9.7781252
Mean0.38159371
Median Absolute Deviation (MAD)0
Skewness2.749117
Sum340
Variance0.64972824
MonotonicityNot monotonic
2024-03-23T16:04:56.543670image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
Histogram with fixed size bins (bins=7)
ValueCountFrequency (%)
0 678
76.1%
1 118
 
13.2%
2 80
 
9.0%
5 5
 
0.6%
3 5
 
0.6%
4 4
 
0.4%
6 1
 
0.1%
ValueCountFrequency (%)
0 678
76.1%
1 118
 
13.2%
2 80
 
9.0%
3 5
 
0.6%
4 4
 
0.4%
5 5
 
0.6%
6 1
 
0.1%
ValueCountFrequency (%)
6 1
 
0.1%
5 5
 
0.6%
4 4
 
0.4%
3 5
 
0.6%
2 80
 
9.0%
1 118
 
13.2%
0 678
76.1%

Ticket
Text

Distinct681
Distinct (%)76.4%
Missing0
Missing (%)0.0%
Memory size55.6 KiB
2024-03-23T16:04:56.781058image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/

Length

Max length18
Median length17
Mean length6.7508418
Min length3

Characters and Unicode

Total characters6015
Distinct characters35
Distinct categories5 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique547 ?
Unique (%)61.4%

Sample

1st rowA/5 21171
2nd rowPC 17599
3rd rowSTON/O2. 3101282
4th row113803
5th row373450
ValueCountFrequency (%)
pc 60
 
5.3%
c.a 27
 
2.4%
a/5 17
 
1.5%
ca 14
 
1.2%
ston/o 12
 
1.1%
2 12
 
1.1%
sc/paris 9
 
0.8%
w./c 9
 
0.8%
soton/o.q 8
 
0.7%
347082 7
 
0.6%
Other values (709) 955
84.5%
2024-03-23T16:04:57.228866image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
3 746
12.4%
1 689
11.5%
2 594
9.9%
7 490
8.1%
4 464
 
7.7%
6 422
 
7.0%
0 406
 
6.7%
5 387
 
6.4%
9 328
 
5.5%
8 282
 
4.7%
Other values (25) 1207
20.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 4808
79.9%
Uppercase Letter 652
 
10.8%
Other Punctuation 295
 
4.9%
Space Separator 239
 
4.0%
Lowercase Letter 21
 
0.3%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
C 151
23.2%
O 100
15.3%
P 98
15.0%
A 82
12.6%
S 74
11.3%
N 40
 
6.1%
T 36
 
5.5%
W 16
 
2.5%
Q 15
 
2.3%
I 11
 
1.7%
Other values (6) 29
 
4.4%
Decimal Number
ValueCountFrequency (%)
3 746
15.5%
1 689
14.3%
2 594
12.4%
7 490
10.2%
4 464
9.7%
6 422
8.8%
0 406
8.4%
5 387
8.0%
9 328
6.8%
8 282
 
5.9%
Lowercase Letter
ValueCountFrequency (%)
a 6
28.6%
s 5
23.8%
r 4
19.0%
i 4
19.0%
l 1
 
4.8%
e 1
 
4.8%
Other Punctuation
ValueCountFrequency (%)
. 197
66.8%
/ 98
33.2%
Space Separator
ValueCountFrequency (%)
239
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 5342
88.8%
Latin 673
 
11.2%

Most frequent character per script

Latin
ValueCountFrequency (%)
C 151
22.4%
O 100
14.9%
P 98
14.6%
A 82
12.2%
S 74
11.0%
N 40
 
5.9%
T 36
 
5.3%
W 16
 
2.4%
Q 15
 
2.2%
I 11
 
1.6%
Other values (12) 50
 
7.4%
Common
ValueCountFrequency (%)
3 746
14.0%
1 689
12.9%
2 594
11.1%
7 490
9.2%
4 464
8.7%
6 422
7.9%
0 406
7.6%
5 387
7.2%
9 328
6.1%
8 282
 
5.3%
Other values (3) 534
10.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 6015
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
3 746
12.4%
1 689
11.5%
2 594
9.9%
7 490
8.1%
4 464
 
7.7%
6 422
 
7.0%
0 406
 
6.7%
5 387
 
6.4%
9 328
 
5.5%
8 282
 
4.7%
Other values (25) 1207
20.1%

Fare
Real number (ℝ)

ZEROS 

Distinct248
Distinct (%)27.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean32.204208
Minimum0
Maximum512.3292
Zeros15
Zeros (%)1.7%
Negative0
Negative (%)0.0%
Memory size7.1 KiB
2024-03-23T16:04:57.469195image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile7.225
Q17.9104
median14.4542
Q331
95-th percentile112.07915
Maximum512.3292
Range512.3292
Interquartile range (IQR)23.0896

Descriptive statistics

Standard deviation49.693429
Coefficient of variation (CV)1.5430725
Kurtosis33.398141
Mean32.204208
Median Absolute Deviation (MAD)6.9042
Skewness4.7873165
Sum28693.949
Variance2469.4368
MonotonicityNot monotonic
2024-03-23T16:04:57.862144image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
8.05 43
 
4.8%
13 42
 
4.7%
7.8958 38
 
4.3%
7.75 34
 
3.8%
26 31
 
3.5%
10.5 24
 
2.7%
7.925 18
 
2.0%
7.775 16
 
1.8%
7.2292 15
 
1.7%
0 15
 
1.7%
Other values (238) 615
69.0%
ValueCountFrequency (%)
0 15
1.7%
4.0125 1
 
0.1%
5 1
 
0.1%
6.2375 1
 
0.1%
6.4375 1
 
0.1%
6.45 1
 
0.1%
6.4958 2
 
0.2%
6.75 2
 
0.2%
6.8583 1
 
0.1%
6.95 1
 
0.1%
ValueCountFrequency (%)
512.3292 3
0.3%
263 4
0.4%
262.375 2
0.2%
247.5208 2
0.2%
227.525 4
0.4%
221.7792 1
 
0.1%
211.5 1
 
0.1%
211.3375 3
0.3%
164.8667 2
0.2%
153.4625 3
0.3%

Cabin
Text

MISSING 

Distinct147
Distinct (%)72.1%
Missing687
Missing (%)77.1%
Memory size33.7 KiB
2024-03-23T16:04:58.668988image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/

Length

Max length15
Median length3
Mean length3.5882353
Min length1

Characters and Unicode

Total characters732
Distinct characters19
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique101 ?
Unique (%)49.5%

Sample

1st rowC85
2nd rowC123
3rd rowE46
4th rowG6
5th rowC103
ValueCountFrequency (%)
c23 4
 
1.7%
c27 4
 
1.7%
g6 4
 
1.7%
b96 4
 
1.7%
b98 4
 
1.7%
f 4
 
1.7%
c25 4
 
1.7%
f33 3
 
1.3%
e101 3
 
1.3%
f2 3
 
1.3%
Other values (151) 201
84.5%
2024-03-23T16:04:59.283345image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
2 72
 
9.8%
C 71
 
9.7%
B 64
 
8.7%
1 61
 
8.3%
3 59
 
8.1%
6 51
 
7.0%
5 45
 
6.1%
4 37
 
5.1%
8 37
 
5.1%
34
 
4.6%
Other values (9) 201
27.5%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 460
62.8%
Uppercase Letter 238
32.5%
Space Separator 34
 
4.6%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
2 72
15.7%
1 61
13.3%
3 59
12.8%
6 51
11.1%
5 45
9.8%
4 37
8.0%
8 37
8.0%
7 34
7.4%
9 33
7.2%
0 31
6.7%
Uppercase Letter
ValueCountFrequency (%)
C 71
29.8%
B 64
26.9%
D 34
14.3%
E 33
13.9%
A 15
 
6.3%
F 13
 
5.5%
G 7
 
2.9%
T 1
 
0.4%
Space Separator
ValueCountFrequency (%)
34
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 494
67.5%
Latin 238
32.5%

Most frequent character per script

Common
ValueCountFrequency (%)
2 72
14.6%
1 61
12.3%
3 59
11.9%
6 51
10.3%
5 45
9.1%
4 37
7.5%
8 37
7.5%
34
6.9%
7 34
6.9%
9 33
6.7%
Latin
ValueCountFrequency (%)
C 71
29.8%
B 64
26.9%
D 34
14.3%
E 33
13.9%
A 15
 
6.3%
F 13
 
5.5%
G 7
 
2.9%
T 1
 
0.4%

Most occurring blocks

ValueCountFrequency (%)
ASCII 732
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
2 72
 
9.8%
C 71
 
9.7%
B 64
 
8.7%
1 61
 
8.3%
3 59
 
8.1%
6 51
 
7.0%
5 45
 
6.1%
4 37
 
5.1%
8 37
 
5.1%
34
 
4.6%
Other values (9) 201
27.5%

Embarked
Categorical

Distinct3
Distinct (%)0.3%
Missing2
Missing (%)0.2%
Memory size50.6 KiB
S
644 
C
168 
Q
77 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters889
Distinct characters3
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowS
2nd rowC
3rd rowS
4th rowS
5th rowS

Common Values

ValueCountFrequency (%)
S 644
72.3%
C 168
 
18.9%
Q 77
 
8.6%
(Missing) 2
 
0.2%

Length

2024-03-23T16:04:59.479819image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-03-23T16:04:59.615457image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
ValueCountFrequency (%)
s 644
72.4%
c 168
 
18.9%
q 77
 
8.7%

Most occurring characters

ValueCountFrequency (%)
S 644
72.4%
C 168
 
18.9%
Q 77
 
8.7%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter 889
100.0%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
S 644
72.4%
C 168
 
18.9%
Q 77
 
8.7%

Most occurring scripts

ValueCountFrequency (%)
Latin 889
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
S 644
72.4%
C 168
 
18.9%
Q 77
 
8.7%

Most occurring blocks

ValueCountFrequency (%)
ASCII 889
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
S 644
72.4%
C 168
 
18.9%
Q 77
 
8.7%

DatasetName
Categorical

CONSTANT 

Distinct1
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size54.1 KiB
train
891 

Length

Max length5
Median length5
Mean length5
Min length5

Characters and Unicode

Total characters4455
Distinct characters5
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowtrain
2nd rowtrain
3rd rowtrain
4th rowtrain
5th rowtrain

Common Values

ValueCountFrequency (%)
train 891
100.0%

Length

2024-03-23T16:04:59.762066image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-03-23T16:04:59.887760image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
ValueCountFrequency (%)
train 891
100.0%

Most occurring characters

ValueCountFrequency (%)
t 891
20.0%
r 891
20.0%
a 891
20.0%
i 891
20.0%
n 891
20.0%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 4455
100.0%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
t 891
20.0%
r 891
20.0%
a 891
20.0%
i 891
20.0%
n 891
20.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 4455
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
t 891
20.0%
r 891
20.0%
a 891
20.0%
i 891
20.0%
n 891
20.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 4455
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
t 891
20.0%
r 891
20.0%
a 891
20.0%
i 891
20.0%
n 891
20.0%

Title
Categorical

Distinct5
Distinct (%)0.6%
Missing0
Missing (%)0.0%
Memory size52.1 KiB
Mr
531 
Miss
187 
Mrs
127 
Master
 
40
Other
 
6

Length

Max length6
Median length2
Mean length2.7620651
Min length2

Characters and Unicode

Total characters2461
Distinct characters9
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowMr
2nd rowMrs
3rd rowMiss
4th rowMrs
5th rowMr

Common Values

ValueCountFrequency (%)
Mr 531
59.6%
Miss 187
 
21.0%
Mrs 127
 
14.3%
Master 40
 
4.5%
Other 6
 
0.7%

Length

2024-03-23T16:05:00.039356image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-03-23T16:05:00.195936image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
ValueCountFrequency (%)
mr 531
59.6%
miss 187
 
21.0%
mrs 127
 
14.3%
master 40
 
4.5%
other 6
 
0.7%

Most occurring characters

ValueCountFrequency (%)
M 885
36.0%
r 704
28.6%
s 541
22.0%
i 187
 
7.6%
t 46
 
1.9%
e 46
 
1.9%
a 40
 
1.6%
O 6
 
0.2%
h 6
 
0.2%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 1570
63.8%
Uppercase Letter 891
36.2%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
r 704
44.8%
s 541
34.5%
i 187
 
11.9%
t 46
 
2.9%
e 46
 
2.9%
a 40
 
2.5%
h 6
 
0.4%
Uppercase Letter
ValueCountFrequency (%)
M 885
99.3%
O 6
 
0.7%

Most occurring scripts

ValueCountFrequency (%)
Latin 2461
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
M 885
36.0%
r 704
28.6%
s 541
22.0%
i 187
 
7.6%
t 46
 
1.9%
e 46
 
1.9%
a 40
 
1.6%
O 6
 
0.2%
h 6
 
0.2%

Most occurring blocks

ValueCountFrequency (%)
ASCII 2461
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
M 885
36.0%
r 704
28.6%
s 541
22.0%
i 187
 
7.6%
t 46
 
1.9%
e 46
 
1.9%
a 40
 
1.6%
O 6
 
0.2%
h 6
 
0.2%

CabinPrefix
Categorical

MISSING 

Distinct8
Distinct (%)0.9%
Missing40
Missing (%)4.5%
Memory size50.5 KiB
F
382 
E
165 
G
109 
C
97 
B
49 
Other values (3)
49 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters851
Distinct characters8
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1 ?
Unique (%)0.1%

Sample

1st rowF
2nd rowC
3rd rowE
4th rowC
5th rowE

Common Values

ValueCountFrequency (%)
F 382
42.9%
E 165
18.5%
G 109
 
12.2%
C 97
 
10.9%
B 49
 
5.5%
D 33
 
3.7%
A 15
 
1.7%
T 1
 
0.1%
(Missing) 40
 
4.5%

Length

2024-03-23T16:05:00.334535image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-03-23T16:05:00.471246image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
ValueCountFrequency (%)
f 382
44.9%
e 165
19.4%
g 109
 
12.8%
c 97
 
11.4%
b 49
 
5.8%
d 33
 
3.9%
a 15
 
1.8%
t 1
 
0.1%

Most occurring characters

ValueCountFrequency (%)
F 382
44.9%
E 165
19.4%
G 109
 
12.8%
C 97
 
11.4%
B 49
 
5.8%
D 33
 
3.9%
A 15
 
1.8%
T 1
 
0.1%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter 851
100.0%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
F 382
44.9%
E 165
19.4%
G 109
 
12.8%
C 97
 
11.4%
B 49
 
5.8%
D 33
 
3.9%
A 15
 
1.8%
T 1
 
0.1%

Most occurring scripts

ValueCountFrequency (%)
Latin 851
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
F 382
44.9%
E 165
19.4%
G 109
 
12.8%
C 97
 
11.4%
B 49
 
5.8%
D 33
 
3.9%
A 15
 
1.8%
T 1
 
0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 851
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
F 382
44.9%
E 165
19.4%
G 109
 
12.8%
C 97
 
11.4%
B 49
 
5.8%
D 33
 
3.9%
A 15
 
1.8%
T 1
 
0.1%

FamilySize
Real number (ℝ)

Distinct9
Distinct (%)1.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.9046016
Minimum1
Maximum11
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size7.1 KiB
2024-03-23T16:05:00.610871image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q11
median1
Q32
95-th percentile6
Maximum11
Range10
Interquartile range (IQR)1

Descriptive statistics

Standard deviation1.6134585
Coefficient of variation (CV)0.84713704
Kurtosis9.159666
Mean1.9046016
Median Absolute Deviation (MAD)0
Skewness2.7274415
Sum1697
Variance2.6032485
MonotonicityNot monotonic
2024-03-23T16:05:00.737538image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
Histogram with fixed size bins (bins=9)
ValueCountFrequency (%)
1 537
60.3%
2 161
 
18.1%
3 102
 
11.4%
4 29
 
3.3%
6 22
 
2.5%
5 15
 
1.7%
7 12
 
1.3%
11 7
 
0.8%
8 6
 
0.7%
ValueCountFrequency (%)
1 537
60.3%
2 161
 
18.1%
3 102
 
11.4%
4 29
 
3.3%
5 15
 
1.7%
6 22
 
2.5%
7 12
 
1.3%
8 6
 
0.7%
11 7
 
0.8%
ValueCountFrequency (%)
11 7
 
0.8%
8 6
 
0.7%
7 12
 
1.3%
6 22
 
2.5%
5 15
 
1.7%
4 29
 
3.3%
3 102
 
11.4%
2 161
 
18.1%
1 537
60.3%
Distinct3
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Memory size56.5 KiB
Single
537 
Small Group
292 
Big Group
62 

Length

Max length11
Median length6
Mean length7.8473625
Min length6

Characters and Unicode

Total characters6992
Distinct characters15
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowSmall Group
2nd rowSmall Group
3rd rowSingle
4th rowSmall Group
5th rowSingle

Common Values

ValueCountFrequency (%)
Single 537
60.3%
Small Group 292
32.8%
Big Group 62
 
7.0%

Length

2024-03-23T16:05:00.871176image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-03-23T16:05:00.996840image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
ValueCountFrequency (%)
single 537
43.1%
group 354
28.4%
small 292
23.5%
big 62
 
5.0%

Most occurring characters

ValueCountFrequency (%)
l 1121
16.0%
S 829
11.9%
i 599
8.6%
g 599
8.6%
n 537
 
7.7%
e 537
 
7.7%
354
 
5.1%
G 354
 
5.1%
r 354
 
5.1%
o 354
 
5.1%
Other values (5) 1354
19.4%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 5393
77.1%
Uppercase Letter 1245
 
17.8%
Space Separator 354
 
5.1%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
l 1121
20.8%
i 599
11.1%
g 599
11.1%
n 537
10.0%
e 537
10.0%
r 354
 
6.6%
o 354
 
6.6%
u 354
 
6.6%
p 354
 
6.6%
m 292
 
5.4%
Uppercase Letter
ValueCountFrequency (%)
S 829
66.6%
G 354
28.4%
B 62
 
5.0%
Space Separator
ValueCountFrequency (%)
354
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 6638
94.9%
Common 354
 
5.1%

Most frequent character per script

Latin
ValueCountFrequency (%)
l 1121
16.9%
S 829
12.5%
i 599
9.0%
g 599
9.0%
n 537
8.1%
e 537
8.1%
G 354
 
5.3%
r 354
 
5.3%
o 354
 
5.3%
u 354
 
5.3%
Other values (4) 1000
15.1%
Common
ValueCountFrequency (%)
354
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 6992
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
l 1121
16.0%
S 829
11.9%
i 599
8.6%
g 599
8.6%
n 537
 
7.7%
e 537
 
7.7%
354
 
5.1%
G 354
 
5.1%
r 354
 
5.1%
o 354
 
5.1%
Other values (5) 1354
19.4%

IsAlone
Categorical

Distinct2
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Memory size56.1 KiB
Alone
537 
With Family
354 

Length

Max length11
Median length5
Mean length7.3838384
Min length5

Characters and Unicode

Total characters6579
Distinct characters14
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowWith Family
2nd rowWith Family
3rd rowAlone
4th rowWith Family
5th rowAlone

Common Values

ValueCountFrequency (%)
Alone 537
60.3%
With Family 354
39.7%

Length

2024-03-23T16:05:01.150423image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-03-23T16:05:01.273100image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
ValueCountFrequency (%)
alone 537
43.1%
with 354
28.4%
family 354
28.4%

Most occurring characters

ValueCountFrequency (%)
l 891
13.5%
i 708
10.8%
A 537
 
8.2%
o 537
 
8.2%
n 537
 
8.2%
e 537
 
8.2%
W 354
 
5.4%
t 354
 
5.4%
h 354
 
5.4%
354
 
5.4%
Other values (4) 1416
21.5%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 4980
75.7%
Uppercase Letter 1245
 
18.9%
Space Separator 354
 
5.4%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
l 891
17.9%
i 708
14.2%
o 537
10.8%
n 537
10.8%
e 537
10.8%
t 354
 
7.1%
h 354
 
7.1%
a 354
 
7.1%
m 354
 
7.1%
y 354
 
7.1%
Uppercase Letter
ValueCountFrequency (%)
A 537
43.1%
W 354
28.4%
F 354
28.4%
Space Separator
ValueCountFrequency (%)
354
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 6225
94.6%
Common 354
 
5.4%

Most frequent character per script

Latin
ValueCountFrequency (%)
l 891
14.3%
i 708
11.4%
A 537
8.6%
o 537
8.6%
n 537
8.6%
e 537
8.6%
W 354
 
5.7%
t 354
 
5.7%
h 354
 
5.7%
F 354
 
5.7%
Other values (3) 1062
17.1%
Common
ValueCountFrequency (%)
354
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 6579
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
l 891
13.5%
i 708
10.8%
A 537
 
8.2%
o 537
 
8.2%
n 537
 
8.2%
e 537
 
8.2%
W 354
 
5.4%
t 354
 
5.4%
h 354
 
5.4%
354
 
5.4%
Other values (4) 1416
21.5%

Interactions

2024-03-23T16:04:52.436684image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2024-03-23T16:04:48.535081image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2024-03-23T16:04:49.290062image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2024-03-23T16:04:50.233541image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2024-03-23T16:04:51.048396image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2024-03-23T16:04:51.750519image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2024-03-23T16:04:52.536382image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2024-03-23T16:04:48.654761image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2024-03-23T16:04:49.426697image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2024-03-23T16:04:50.386163image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2024-03-23T16:04:51.155107image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2024-03-23T16:04:51.855235image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2024-03-23T16:04:52.660087image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2024-03-23T16:04:48.787427image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2024-03-23T16:04:49.576298image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2024-03-23T16:04:50.524793image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2024-03-23T16:04:51.279777image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2024-03-23T16:04:51.983895image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2024-03-23T16:04:52.787745image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2024-03-23T16:04:48.929059image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2024-03-23T16:04:49.798734image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2024-03-23T16:04:50.657407image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2024-03-23T16:04:51.402446image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2024-03-23T16:04:52.102542image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2024-03-23T16:04:52.904400image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2024-03-23T16:04:49.045750image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2024-03-23T16:04:49.927358image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2024-03-23T16:04:50.788093image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2024-03-23T16:04:51.515144image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2024-03-23T16:04:52.220229image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2024-03-23T16:04:53.016100image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2024-03-23T16:04:49.168388image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2024-03-23T16:04:50.049034image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2024-03-23T16:04:50.914720image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2024-03-23T16:04:51.638783image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2024-03-23T16:04:52.330964image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/

Missing values

2024-03-23T16:04:53.186645image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
A simple visualization of nullity by column.
2024-03-23T16:04:53.469887image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.
2024-03-23T16:04:53.654395image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
The correlation heatmap measures nullity correlation: how strongly the presence or absence of one variable affects the presence of another.

Sample

PassengerIdSurvivedPclassNameSexAgeSibSpParchTicketFareCabinEmbarkedDatasetNameTitleCabinPrefixFamilySizeTicketAppearancesIsAlone
010.03Braund, Mr. Owen Harrismale22.010A/5 211717.2500NaNStrainMrF2Small GroupWith Family
121.01Cumings, Mrs. John Bradley (Florence Briggs Thayer)female38.010PC 1759971.2833C85CtrainMrsC2Small GroupWith Family
231.03Heikkinen, Miss. Lainafemale26.000STON/O2. 31012827.9250NaNStrainMissE1SingleAlone
341.01Futrelle, Mrs. Jacques Heath (Lily May Peel)female35.01011380353.1000C123StrainMrsC2Small GroupWith Family
450.03Allen, Mr. William Henrymale35.0003734508.0500NaNStrainMrE1SingleAlone
560.03Moran, Mr. JamesmaleNaN003308778.4583NaNQtrainMrE1SingleAlone
670.01McCarthy, Mr. Timothy Jmale54.0001746351.8625E46StrainMrE1SingleAlone
780.03Palsson, Master. Gosta Leonardmale2.03134990921.0750NaNStrainMasterG5Big GroupWith Family
891.03Johnson, Mrs. Oscar W (Elisabeth Vilhelmina Berg)female27.00234774211.1333NaNStrainMrsE3Small GroupWith Family
9101.02Nasser, Mrs. Nicholas (Adele Achem)female14.01023773630.0708NaNCtrainMrsF2Small GroupWith Family
PassengerIdSurvivedPclassNameSexAgeSibSpParchTicketFareCabinEmbarkedDatasetNameTitleCabinPrefixFamilySizeTicketAppearancesIsAlone
8818820.03Markun, Mr. Johannmale33.0003492577.8958NaNStrainMrF1SingleAlone
8828830.03Dahlberg, Miss. Gerda Ulrikafemale22.000755210.5167NaNStrainMissE1SingleAlone
8838840.02Banfield, Mr. Frederick Jamesmale28.000C.A./SOTON 3406810.5000NaNStrainMrF1SingleAlone
8848850.03Sutehall, Mr. Henry Jrmale25.000SOTON/OQ 3920767.0500NaNStrainMrF1SingleAlone
8858860.03Rice, Mrs. William (Margaret Norton)female39.00538265229.1250NaNQtrainMrsG6Big GroupWith Family
8868870.02Montvila, Rev. Juozasmale27.00021153613.0000NaNStrainMrF1SingleAlone
8878881.01Graham, Miss. Margaret Edithfemale19.00011205330.0000B42StrainMissB1SingleAlone
8888890.03Johnston, Miss. Catherine Helen "Carrie"femaleNaN12W./C. 660723.4500NaNStrainMissG4Small GroupWith Family
8898901.01Behr, Mr. Karl Howellmale26.00011136930.0000C148CtrainMrC1SingleAlone
8908910.03Dooley, Mr. Patrickmale32.0003703767.7500NaNQtrainMrF1SingleAlone